load packages
library(ggplot2)
library(readxl)
library(readr)
library(tidyverse)
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## ✔ dplyr 1.1.4 ✔ stringr 1.5.1
## ✔ forcats 1.0.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
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## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
library(sf)
## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
library(plotly)
##
## Attaching package: 'plotly'
##
## The following object is masked from 'package:ggplot2':
##
## last_plot
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following object is masked from 'package:graphics':
##
## layout
library(geojsonio)
## Registered S3 method overwritten by 'geojsonsf':
## method from
## print.geojson geojson
##
## Attaching package: 'geojsonio'
##
## The following object is masked from 'package:base':
##
## pretty
load dataset
df_descriptive=read_csv("filtered_merged_dataset_sample.csv")
## Rows: 10000 Columns: 20
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): BORO, PERP_AGE_GROUP, PERP_SEX, PERP_RACE, VIC_AGE_GROUP, VIC_SEX...
## dbl (10): INCIDENT_KEY, PRECINCT, Latitude, Longitude, Number_poverty, Perc...
## date (1): OCCUR_DATE
## time (1): OCCUR_TIME
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
data_final <- read_csv("data_final.csv")
## Rows: 9820 Columns: 40
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (17): BORO, LOC_OF_OCCUR_DESC, LOC_CLASSFCTN_DESC, LOCATION_DESC, PERP_...
## dbl (15): INCIDENT_KEY, PRECINCT, JURISDICTION_CODE, X_COORD_CD, Y_COORD_CD...
## num (2): Number_poverty, Number_education
## lgl (3): STATISTICAL_MURDER_FLAG, Is_Holiday, Sky_Is_Dark
## dttm (1): OCCUR_DATETIME
## date (1): OCCUR_DATE
## time (1): OCCUR_TIME
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Plot of the number of incidents in each borough for each year
# Summarize data: count the number of incidents by borough and year
incident_summary <- df_descriptive %>%
group_by(BORO, Year) %>%
summarise(Number_of_Incidents = n(), .groups = "drop") %>%
# Ensure missing years and boroughs are included
complete(BORO, Year = full_seq(min(df_descriptive$Year):max(df_descriptive$Year), 1), fill = list(Number_of_Incidents = 0))
# Create the bar plot
ggplot(incident_summary, aes(x = Year, y = Number_of_Incidents, fill = BORO)) +
geom_bar(stat = "identity", position = "dodge") +
labs(
title = "Number of Incidents in Each Borough by Year",
x = "Year",
y = "Number of Incidents",
fill = "Borough"
) +
scale_x_continuous(breaks = seq(min(df_descriptive$Year), max(df_descriptive$Year), by = 1)) +
theme_minimal()
##Total incidents per NTA
Load spatial data (replace with actual shapefile path)
nta_shape <- st_read("nynta2020_24d/nynta2020.shp")
## Reading layer `nynta2020' from data source
## `/Users/wangmingyin/Desktop/data science 1/nyc_shooting_final/nynta2020_24d/nynta2020.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 262 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 913175.1 ymin: 120128.4 xmax: 1067383 ymax: 272844.3
## Projected CRS: NAD83 / New York Long Island (ftUS)
cdta_shape = st_read("nycdta2020_24d/nycdta2020.shp")
## Reading layer `nycdta2020' from data source
## `/Users/wangmingyin/Desktop/data science 1/nyc_shooting_final/nycdta2020_24d/nycdta2020.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 71 features and 8 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 913175.1 ymin: 120128.4 xmax: 1067383 ymax: 272844.3
## Projected CRS: NAD83 / New York Long Island (ftUS)
boro_shape = st_read("Borough Boundaries/geo_export_391a75ed-0ae4-4c88-8c30-3588c75bd01e.shp")
## Reading layer `geo_export_391a75ed-0ae4-4c88-8c30-3588c75bd01e' from data source `/Users/wangmingyin/Desktop/data science 1/nyc_shooting_final/Borough Boundaries/geo_export_391a75ed-0ae4-4c88-8c30-3588c75bd01e.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 5 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -74.25559 ymin: 40.49613 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS: WGS84(DD)
# Prepare incident data: count incidents per NTA_clean
nta_incident_counts <- data_final %>%
group_by(NTA) %>%
summarise(Number_of_Incidents = n(), .groups = "drop")
# Merge spatial data with incident counts
nta_map_data <- nta_shape %>%
left_join(nta_incident_counts, by = c("NTAName" = "NTA"))
# Create custom breaks for Number_of_Incidents
nta_map_data <- nta_map_data %>%
mutate(
Incident_Range = cut(
Number_of_Incidents,
breaks = seq(0, 400, by = 80), # Breaks from 0 to 1000, every 200 cases
labels = c("0-80", "81-160", "161-240", "241-320", "321-400"),
include.lowest = TRUE
)
)
Plot the map
# Plot the map with custom ranges
ggplot(data = nta_map_data) +
geom_sf(aes(fill = Incident_Range), color = "white", size = 0.2) +
geom_sf_text(aes(label = Number_of_Incidents), size = 3, color = "black") + # Add labels
scale_fill_manual(
values = c(
"0-80" = "#b2e2e2",
"81-160" = "skyblue",
"161-240" = "#66c2a4",
"241-320" = "#2ca25f",
"321-400" = "#006d2c"
),
name = "Number of Incidents"
) +
labs(
title = "Total Number of Incidents Across NYC NTAs from 2017 to 2023",
subtitle = "Incidents grouped by range (0-400, 80 breaks)",
caption = "Data Source: Your dataset"
) +
theme_minimal() +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank()
)
## Warning: Removed 41 rows containing missing values or values outside the scale range
## (`geom_text()`).
unmatched_nta <- setdiff(nta_shape$NTAName, data_final$NTA)
unmatched_nta
## [1] "Brooklyn Heights"
## [2] "Windsor Terrace-South Slope"
## [3] "Fort Hamilton"
## [4] "Dyker Beach Park"
## [5] "Mapleton-Midwood (West)"
## [6] "Calvert Vaux Park"
## [7] "Holy Cross Cemetery"
## [8] "McGuire Fields"
## [9] "Jamaica Bay (West)"
## [10] "Shirley Chisholm State Park"
## [11] "North & South Brother Islands"
## [12] "Hart Island"
## [13] "The Battery-Governors Island-Ellis Island-Liberty Island"
## [14] "Stuyvesant Town-Peter Cooper Village"
## [15] "United Nations"
## [16] "Rikers Island"
## [17] "Astoria Park"
## [18] "Sunnyside Yards (South)"
## [19] "Calvary & Mount Zion Cemeteries"
## [20] "Mount Olivet & All Faiths Cemeteries"
## [21] "Middle Village Cemetery"
## [22] "St. John Cemetery"
## [23] "Rego Park"
## [24] "Bay Terrace-Clearview"
## [25] "Fort Totten"
## [26] "Kissena Park"
## [27] "Mount Hebron & Cedar Grove Cemeteries"
## [28] "Cunningham Park"
## [29] "Spring Creek Park"
## [30] "Douglaston-Little Neck"
## [31] "Montefiore Cemetery"
## [32] "Breezy Point-Belle Harbor-Rockaway Park-Broad Channel"
## [33] "LaGuardia Airport"
## [34] "Jamaica Bay (East)"
## [35] "Jacob Riis Park-Fort Tilden-Breezy Point Tip"
## [36] "Oakwood-Richmondtown"
## [37] "Freshkills Park (South)"
## [38] "Fort Wadsworth"
## [39] "Hoffman & Swinburne Islands"
## [40] "Miller Field"
## [41] "Great Kills Park"
summarize CDTA and BORO
## There is space between letter and number in CDTA, I deleted the space below
data_final$CDTA <- gsub(" ", "", data_final$CDTA)
cdta_incident_counts <- data_final %>%
group_by(CDTA) %>%
summarise(Number_of_Incidents = n(), .groups = "drop")
merge datasets
# Prepare incident data: count incidents per NTA_clean
cdta_incident_counts <- data_final %>%
group_by(CDTA) %>%
summarise(Number_of_Incidents = n(), .groups = "drop")
# Merge spatial data with incident counts
cdta_map_data <- cdta_shape %>%
left_join(cdta_incident_counts, by = c("CDTA2020" = "CDTA"))
# Create custom breaks for Number_of_Incidents
cdta_map_data <- cdta_map_data %>%
mutate(
Incident_Range = cut(
Number_of_Incidents,
breaks = seq(0, 400, by = 80), # Breaks from 0 to 1000, every 200 cases
labels = c("0-80", "81-160", "161-240", "241-320", "321-400"),
include.lowest = TRUE
)
)
# Plot the map with custom ranges
ggplot(data = cdta_map_data) +
geom_sf(aes(fill = Incident_Range), color = "white", size = 0.2) +
geom_sf_text(aes(label = Number_of_Incidents), size = 3, color = "black") + # Add labels
scale_fill_manual(
values = c(
"0-80" = "#b2e2e2",
"81-160" = "skyblue",
"161-240" = "#66c2a4",
"241-320" = "#2ca25f",
"321-400" = "#006d2c"
),
name = "Number of Incidents"
) +
labs(
title = "Total Number of Incidents Across NYC CDTAs from 2017 to 2023",
subtitle = "Incidents grouped by range (0-400, 80 breaks)",
caption = "Data Source: Your dataset"
) +
theme_minimal() +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
axis.title.x = element_blank(), # Remove x-axis label
axis.title.y = element_blank()
)
## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_text()`).
# Count the boro incident
boro_incident_counts <- data_final %>%
group_by(BORO) %>%
summarise(Number_of_Incidents = n(), .groups = "drop") %>%
mutate(BORO = tolower(BORO) )
# Lowercase the boro in boro_shape
boro_shape = boro_shape %>%
mutate(boro_name = tolower(boro_name))
# Merge spatial data with incident counts
boro_map_data <- boro_shape %>%
left_join(boro_incident_counts, by = c("boro_name" = "BORO"))
# Create custom breaks for Number_of_Incidents
boro_map_data <- boro_map_data %>%
mutate(
Incident_Range = cut(
Number_of_Incidents,
breaks = seq(0, 4000, by = 800), # Breaks from 0 to 4000, every 800 cases
labels = c("0-800", "801-1600", "1601-2400", "2401-3200", "3201-4000"),
include.lowest = TRUE
)
)
# Plot the map with custom ranges
ggplot(data = boro_map_data) +
geom_sf(aes(fill = Incident_Range), color = "white", size = 0.2) +
geom_sf_text(aes(label = Number_of_Incidents), size = 3, color = "black") + # Add labels
scale_fill_manual(
values = c(
"0-800" = "#b2e2e2",
"801-1600" = "skyblue",
"1601-2400" = "#66c2a4",
"2401-3200" = "#2ca25f",
"3201-4000" = "#006d2c"
),
name = "Number of Incidents"
) +
labs(
title = "Total Number of Incidents Across NYC BOROs from 2017 to 2023",
subtitle = "Incidents grouped by range (0-4000, 800 breaks)",
caption = "Data Source: Your dataset"
) +
theme_minimal() +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
axis.title.x = element_blank(), # Remove x-axis label
axis.title.y = element_blank()
)
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
Plotly of boro map
boro_map_data <- boro_map_data %>%
mutate(
hover_text = paste("Borough:", boro_name, "<br>Total Incidents:", Number_of_Incidents)
)
# Create the interactive plot with click functionality
plot <- plot_ly(
data = boro_map_data,
type = "scattermapbox",
split = ~boro_name, # Separate polygons by boroughs
color = ~Number_of_Incidents, # Color based on the number of incidents
colors = "viridis", # Use a color scale
text = ~hover_text, # Display hover text
hoverinfo = "text",
marker = list(size = 8, opacity = 0.7)
) %>%
layout(
title = "Total Number of Incidents Across NYC BOROs (2017-2023)",
mapbox = list(
style = "carto-positron", # Base map style
center = list(lon = -74.0060, lat = 40.7128), # Center map on NYC
zoom = 10
)
)
# Add click functionality to display the borough name and number of incidents
plot <- plot %>%
event_register("plotly_click") %>%
htmlwidgets::onRender("
function(el, x) {
el.on('plotly_click', function(d) {
var point = d.points[0];
var text = point.text;
alert('You clicked on: ' + text);
});
}
")
# Display the interactive plot
plot
## No scattermapbox mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
## A marker object has been specified, but markers is not in the mode
## Adding markers to the mode...
## A marker object has been specified, but markers is not in the mode
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plotly for CDTA
# Prepare CDTA-level data
cdta_map_data <- cdta_map_data %>%
mutate(
hover_text = paste("CDTA:", CDTA2020, "<br>Total Incidents:", Number_of_Incidents)
)
# Create the interactive plot with click functionality
plot <- plot_ly(
data = cdta_map_data,
type = "scattermapbox",
split = ~CDTA2020, # Separate polygons by boroughs
color = ~Number_of_Incidents, # Color based on the number of incidents
colors = "viridis", # Use a color scale
text = ~hover_text, # Display hover text
hoverinfo = "text",
marker = list(size = 8, opacity = 0.7)
) %>%
layout(
title = "Total Number of Incidents Across NYC CDTAs (2017-2023)",
mapbox = list(
style = "carto-positron", # Base map style
center = list(lon = -74.0060, lat = 40.7128), # Center map on NYC
zoom = 10
)
)
# Add click functionality to display the borough name and number of incidents
plot <- plot %>%
event_register("plotly_click") %>%
htmlwidgets::onRender("
function(el, x) {
el.on('plotly_click', function(d) {
var point = d.points[0];
var text = point.text;
alert('You clicked on: ' + text);
});
}
")
# Display the interactive plot
plot
## No scattermapbox mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
## Warning: line.color doesn't (yet) support data arrays
## Warning: Only one fillcolor per trace allowed
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# Remove any trailing spaces or mismatches in CDTA identifiers:
cdta_shape$CDTA2020 <- gsub(" ", "", cdta_shape$CDTA2020)
data_final$CDTA <- gsub(" ", "", data_final$CDTA)
# Identify Missing Matches
unmatched_cdta <- setdiff(cdta_shape$CDTA2020, data_final$CDTA)
print(unmatched_cdta) # These are the CDTAs that are missing from the dataset
## [1] "QN80" "SI95" "QN84"
# Assign 0 to Missing Areas
cdta_incident_counts <- data_final %>%
group_by(CDTA) %>%
summarise(Number_of_Incidents = n(), .groups = "drop") %>%
complete(CDTA = unique(cdta_shape$CDTA2020), fill = list(Number_of_Incidents = 0))
#Re-Merge the Data
cdta_map_data <- cdta_shape %>%
left_join(cdta_incident_counts, by = c("CDTA2020" = "CDTA"))
# Update NA Handling
cdta_map_data <- cdta_map_data %>%
mutate(
Number_of_Incidents = ifelse(is.na(Number_of_Incidents), 0, Number_of_Incidents),
Incident_Range = cut(
Number_of_Incidents,
breaks = seq(0, 600, by = 120),
labels = c("0-120", "121-240", "241-360", "361-480", "481-600"),
include.lowest = TRUE
)
)
ggplot(data = cdta_map_data) +
geom_sf(aes(fill = Incident_Range), color = "white", size = 0.2) +
geom_sf_text(aes(label = Number_of_Incidents), size = 3, color = "black") + # Add labels+
scale_fill_manual(
values = c(
"0-120" = "#b2e2e2",
"121-240" = "skyblue",
"241-360" = "#66c2a4",
"361-480" = "#2ca25f",
"481-600" = "#006d2c"
),
name = "Number of Incidents"
) +
labs(
title = "Total Number of Incidents Across NYC CDTAs from 2017 to 2023",
subtitle = "Incidents grouped by range (0-600, 120 breaks)",
caption = "Data Source: Your dataset"
) +
theme_minimal() +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
check unmatch CDTA
unmatched <- cdta_shape %>%
filter(!CDTA2020 %in% data_final$CDTA)
print(unmatched) # This shows unmatched areas
## Simple feature collection with 3 features and 8 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 945159.7 ymin: 132138 xmax: 1049020 ymax: 225678
## Projected CRS: NAD83 / New York Long Island (ftUS)
## BoroCode BoroName CountyFIPS CDTA2020
## 1 4 Queens 081 QN80
## 2 5 Staten Island 085 SI95
## 3 4 Queens 081 QN84
## CDTAName CDTAType
## 1 QN80 LaGuardia Airport (JIA 80 Approximation) 1
## 2 SI95 Great Kills Park-Fort Wadsworth (JIA 95 Approximation) 1
## 3 QN84 Jamaica Bay (East) (JIA 84 Approximation) 1
## Shape_Leng Shape_Area geometry
## 1 42122.47 30591949 MULTIPOLYGON (((1019455 225...
## 2 80517.91 44747671 MULTIPOLYGON (((951604.1 13...
## 3 239900.06 122558581 MULTIPOLYGON (((1022140 148...
# Filter out borough
boroughs <- unique(cdta_map_data$BoroName)
for (b in boroughs) {
borough_data <- cdta_map_data %>%
filter(BoroName == b)
plot <- ggplot(data = borough_data) +
geom_sf(aes(fill = Number_of_Incidents), color = "black") +
geom_sf_text(aes(label = Number_of_Incidents), size = 3, color = "white") + # Add labels+
scale_fill_gradientn(
colors = c("blue", "green", "yellow", "red"), # Custom color scale
name = "Number of Incidents"
) +
labs(
title = paste("CDTA Incidents in", b),
subtitle = "2017 to 2023",
x = "Longitude",
y = "Latitude"
) +
theme_minimal()
print(plot) # Move inside the loop
}